Direct lift control for aircraft has been around in the aeronautical industry for decades but is mainly used in commercial aircraft with dedicated direct lift control surfaces. The focus of this thesis is to investigate if direct lift control is feasible for a fighter aircraft, similar to Saab JAS 39 Gripen, without dedicated control surfaces. The modelled system is an aircraft that is inherently unstable and contains nonlinearities both in its aerodynamics and in the form of limited control surface deflection and deflection rates. The dynamics of the aircraft are linearised around a flight case representative of a landing scenario. Direct lift control is then applied to give a more immediate relation from pilot stick input to change in flight path angle while also preserving the pitch attitude. Two different control strategies, linear quadratic control and model predictive control, were chosen for the implementation. Since fighter aircraft are systems with fast dynamics it was important to limit the computational time. This constraint motivated the use of specialised methods to speed up the optimisation of the model predictive controller. Results from simulations in a nonlinear simulation environment supplied by Saab, as well as tests in high-fidelity flight simulation rigs with a pilot, proved that direct lift control is feasible for the investigated fighter aircraft. Sufficient control authority and performance when controlling the flight path angle were observed. Both developed controllers have their own advantages and which strategy is the most suitable depends on what the user prioritises. Pilot workload during landing as well as precision at touch down were deemed similar to conventional control.

In this work we present a novel computational framework for analyzing evolution of uncertainty in state trajectories of a hypersonic air vehicle due to uncertainty in initial conditions and other system parameters. The framework is built on the so called generalized Polynomial Chaos expansions. In this framework, stochastic dynamical systems are transformed into equivalent deterministic dynamical systems in higher dimensional space. In the research presented here we study evolution of uncertainty due to initial condition, ballistic coefficient, lift over drag ratio and atmospheric density. We compute the statistics using the continuous linearization (CL) approach. This approach computes the jacobian of the perturbational variables about the nominal trajectory. The covariance is then propagated using the riccati equation and the statistics is compared with the Polynomial Chaos method. The latter gives better accuracy as compared to the CL method. The simulation is carried out assuming uniform distribution on the parameters (initial condition, density, ballistic coefficient and lift over drag ratio). The method is then extended for Gaussian distribution on the parameters and the statistics, mean and variance of the states are matched with the standard Monte Carlo methods. The problem studied here is related to the Mars entry descent landing problem.